1. Field of the Invention
This invention relates to layer processing and to a method and a system therefor, and more particularly (although not exclusively) to such a method and system for processing semiconductor material layers.
2. Discussion of Prior Art
In techniques for processing materials in thin layers inter alia for integrated circuits, there is a well-known problem of controlling linear dimensions (eg layer thickness or etch depth) and chemical composition in real time, ie during growth of a layer or while etching a surface. This problem is particularly relevant to growing layers of materials by low pressure vapour phase epitaxy (LPVPE). A degree of control over growth is available by controlling the relative proportions of partial pressures of constituent gases in a LPVPE source gas stream to be decomposed to produce a deposited layer. The prearranged sequence of gas mixtures, substrate temperatures and growth times is included in a growth recipe, the proportions of which should at least approximately be preserved in a layer grown from it. However, in practice there is drift in the calibration of gas flow control apparatus, which means growth diverges from the prearranged recipe, and the composition of a growing layer can alter unless there is some means for monitoring and controlling the layer composition and thickness in real time. Unfortunately it is not possible to make direct measurements of the parameters of layer thickness and chemical composition in real time during growth. To do so it is necessary to interrupt growth of a specimen and remove it from growth apparatus, which is most unattractive because it is time consuming, it interrupts the growth process and it may contaminate the layer being grown.
As a partial and indirect approach to solving the problem of monitoring chemical composition and linear dimensions in real time, it is known to use spectroscopic ellipsometry. Ellipsometry can be used for measurements on a growing specimen or a specimen being etched, but it does not give the necessary composition and thickness information directly. It uses reflection of light from a specimen surface to give optical parameters, but these involve a convolution of dimensions and refractive index which cannot be separated. This problem occurs in the growth of alloys such as silicon germanium alloy (Si1xe2x88x92xGex), where it is important to have accurate control over the alloy composition parameter x as well as layer thickness. It is particularly important in the growth of superlattices where the composition parameter x in a material system such as Si1xe2x88x92xGex alternates between successive layers in the region of eg 100 Angstroms thick.
However, it is possible to infer dimensions and composition from ellipsometric measurements combined with information from the chemical process taking place, eg a model of the process of a material being etched or of LPVPE layer growth derived from the gas mixture recipe. This again leads to further difficulties in practice because successive ellipsometric measurements taken at time intervals in the region of 1 or 2 seconds may be inaccurate or xe2x80x9cnoisyxe2x80x9d, and do not necessarily give acceptable results for layer process control except under favourable circumstances.
In Appl. Phys. Lett. 57 (25), December 1990, Aspnes et al described optical control of growth of AlxGa1xe2x88x92xAs by organometallic molecular beam epitaxy. They disclosed a closed-loop control system for epitaxial growth of a homogeneous semiconductor crystal using monochromatic ellipsometry. The system related to homogeneous growth of a single layer where composition was controlled to remain constant. There was no disclosure of control of layer thickness.
In Thin Solid Films, 220, 1992, Urban reported development of artificial neural networks for real time in-situ ellipsometry data reduction They described monochromatic ellipsometry for a single homogeneous layer grown upon a substrate. The neural network was trained to provide seed values for an iterative model fitting routine which fit the layer composition and thickness parameters to the measured ellipsometric angles. There was no disclosure of using the resulting estimate to control growth.
In Thin Solid Films, 223, 1993, Johs et al describe using multi-wavelength ellipsometry for real-time monitoring and control during growth of CdTe by metal-organic vapour phase epitaxy (MOVPE). They disclosed making ellipsometry measurements at twelve wavelengths in less than three seconds. They introduced the virtual-interface method for determining the characteristics of the near-surface region of the growing crystal. They wished to estimate the rate of growth of homogeneous material (constant composition). The estimates of near-surface layer composition were obtained by fitting the parameters of the virtual-interface model (dielectric constants and layer depth) using an iterative model fitting algorithm. This relied upon using the algorithm to fit layer composition parameters to the most recent ellipsometry measurements.
In Applied Surface Science 63 (1993) pp 9-16, Duncan and Henck describe an etching process using a specimen with a known refractive index; ellipsometric measurements then gave thickness or etch depth directly. The specimen was SiO2 2000 Angstroms thick upon an Si substrate. The etch depth measurements had uncertainties in the range 3 to 23 Angstroms. Measurements were made every 2 seconds approximately, and about 100 seconds were needed to etch through the specimen, so the incremental etch depth between measurements was 40 Angstroms. In consequence the uncertainty in the incremental etch depth varied between 15% and 57%, despite the SiO2/Si system being favourable for ellipsometric measurements; these materials have very different refractive indices of 1.4 and 3.9 respectively at 2 eV, and they are therefore easily discriminated by optical measurements.
In layer growth processing of compounds, eg alloys such as Si1xe2x88x92xGex, it is desirable to determine the thickness and composition of the layer contribution grown between successive pairs of ellipsometric measurements at intervals of 1-2 seconds. Si1xe2x88x92xGex, is grown at a rate of about 1 Angstrom per second, so layer contributions are 1-2 Angstroms thick. Since the composition of the layer contribution is unknown so also is the refractive index, and therefore the thickness cannot be determined directly. Si and Ge have similar refractive indices, eg 3.9 and 4 at 2 eV, and consequently the refractive index of Si1xe2x88x92xGex, is not very sensitive to changes in x and ellipsometric measurements give more inaccurate results than those for the SiO2/Si system.
In Diagnostic Techniques for Semiconductor Materials Processing II, Pang et. al. Eds, pp 87-94, Materials Research Soc., Pittsburgh, Pa. 1996, Vincent et. al. presented a method for in-situ estimation of etch rate using an extended Kalman filter based method for multi-wavelength reflectometry. Its possible application in real-time control was referred to but implementation details were not disclosed.
In the International Conference on Characterisation and Metrology for VLSI Technology, Gaithersburg, Md. USA, March 1998, Pickering et al disclose real-time process control using spectroscopic ellipsometry for Si/SiGe epitaxy. Si1xe2x88x92xGex was grown with x in the range 0 to 0.2xe2x80x94ie variable composition. They discussed the composition/growth rate correlation problem required for control of very thin near-surface layers. It was suggested that a principal component analysis algorithm might be used to obtain an estimate of growth rate which is independent of alloy composition. Moreover an analysis of composition based on an artificial neural network algorithm was given which was compared with SIMS data; after adjustment by scaling to allow for the lack of growth rate data given by this approach, a discrepancy of 0.02 for x in the range 0 to 0.2 was obtained, ie an error of at least 10% even when scaled.
It is an object of the invention to provide an alternative method and apparatus for layer processing.
The present invention provides a method of layer processing comprising applying material to a surface to etch it or produce growth upon it, monitoring the surface with a sensor to provide an output indicating change therein, and using the output in generating a control input for layer processing, characterised in that the control input is generated partly on the basis of the sensor output and partly on the basis of change prediction from surface state prior to the change and processing conditions responsible for it.
The invention has the advantage that it does not rely on a potentially inaccurate or noisy sensor output as the only determinant of control. Instead it incorporates prediction based on prior surface and processing, which has the effect of counteracting sensor measurement uncertainty.
The control input is preferably generated in accordance with a Bayesian algorithm taking into account change probability based on prior surface state and material supply.
In a preferred embodiment, the method of the invention comprises growing a layer structure upon a heated substrate surface, comprising supplying a vapour mixture stream to the substrate for decomposition of stream constituents and selective deposition, monitoring growth upon the surface with an ellipsometric sensor providing an output in response, and using sensor output in controlling growth, characterised in that the method includes controlling growth partly on the basis of the sensor output and partly on the basis of change prediction from surface growth status prior to the change and stream constituents and substrate temperature responsible for it. A Bayesian algorithm may be employed in control of growth modelled as deposition of successive pseudo-layers upon the substrate to provide for the prediction of the composition and thickness parameters of a subsequent pseudo-layer on the basis of the stream constituents and substrate temperature during its growth and the composition of the respective preceding pseudo-layer.
The method of the invention may include growing successive pseudo-layers upon the substrate surface, together with the following steps undertaken during growth:
a) deriving a predicted probability density function for pseudo-layer growth parameters from initial surface composition, growth conditions and growth probabilities associated therewith, the function comprising discrete samples;
b) assigning weight values to the discrete samples in accordance with respective occurrence likelihoods derived on the basis of most recent sensor output;
c) selecting a subset of the discrete samples, selection likelihood being weighted in favour of samples with greater weight values;
d) using the subset to provide a subsequent predicted probability density function and controlling growth in accordance with the pseudo-layer growth parameters indicated thereby,
e) iterating steps (b) to (e) until growth is complete.
In an alternative aspect, the present invention provides an apparatus for layer processing comprising means for applying material to a surface to etch it or produce growth upon it, the apparatus having predetermined processing characteristics in terms of material supply and surface condition, a sensor for monitoring the surface to provide an output indicating change therein, and control means for layer processing control responsive to the sensor output, characterised in that the control means is operative partly on the basis of the sensor output and partly on the basis of change prediction from surface state prior to the change and processing conditions responsible for it. The control input may be generated in accordance with a Bayesian algorithm taking into account change probability based on prior surface state and material supply.
The apparatus may be arranged to grow a layer structure upon a heated substrate surface, and comprise means for supplying a vapour mixture stream to the substrate for decomposition of stream constituents and selective deposition, an ellipsometric sensor for monitoring growth upon the surface and providing an output in response, and growth control means responsive to the sensor output, characterised in that the growth control means is operative partly on the basis of the sensor output and partly on the basis of change prediction from surface growth status prior to the change and stream constituents and substrate temperature responsible for it. The growth control means may employ a Bayesian algorithm in control of growth modelled as deposition of successive pseudo-layers upon the substrate surface, and provide for the prediction of the composition and thickness parameters of a subsequent pseudo-layer on the basis of the stream constituents and substrate temperature during its growth and the composition of the respective preceding pseudo-layer.
The apparatus may be arranged to grow successive pseudo-layers upon the substrate surface, and the growth control means is arranged to carry out the following steps undertaken during growth:
a) derive a predicted probability density function for pseudo-layer growth parameters from initial surface composition, growth conditions and growth probabilities associated therewith, the function comprising discrete samples;
b) assign weight values to the discrete samples in accordance with respective occurrence likelihoods derived on the basis of most recent sensor output;
c) select a subset of the discrete samples, selection likelihood being weighted in favour of samples with greater weight values;
d) use the subset to provide a subsequent predicted probability density function and controlling growth in accordance with the pseudo-layer growth parameters indicated thereby,
e) iterate steps (b) to (e) until growth is complete.